1. Field of the Invention
The present invention relates, in general, to simultaneous localization and mapping using a structured light distance sensor, and more particularly, to a method and apparatus that process a line pattern from an image generated by structured light, thereby eliminating noise from the image.
2. Description of Related Art
A mobile robot capable of moving by itself has a wide working area, such as an outdoor area at a fixed location, or a previously structured working environment, and therefore, it is more useful than a fixed robot from the standpoint of its functionality and variable applicability. Recent mobile robots undertake the work of humans in places where humans cannot easily go, such as the deep sea or radioactive areas, thus greatly contributing to the safety of human beings.
A mobile robot needs to have capabilities, such as automatic path planning, obstacle detection and collision avoidance, which are required to perform work in a place where previously supplied information about the surrounding environment is insufficient. Recently, research has been actively conducted on “Simultaneous Localization and Map Building (SLAM)” that is technology for simultaneously automatically building a map and localizing a robot while the robot moves to locations where previously supplied information is insufficient.
In order for a mobile robot to perform SLAM, it is essential to have the ability to obtain information used for localization from the surrounding environment. Various methods using a vision sensor, an ultrasonic wave sensor, a touch sensor, and others have been applied to SLAM. In particular, a vision sensor using structured light, such as a laser, and a camera, and a three-dimensional recognition algorithm based on the visual sensor reduce the burden of calculation and can be used in a place where the variation in brightness is severe, so that the visual sensor and the three-dimensional recognition algorithm are regarded as a very efficient method.
Such a method is performed by radiating light onto an object in a predetermined form such as laser light, and obtaining an image using a sensor such as a camera. Thereafter, if the location at which laser light is reflected is captured by an obtained image, the distance between the location at which laser light is emitted and the location at which laser light is reflected can be calculated through a triangulation method based on the coordinates of a camera image, a scan angle at the location, and the distance between the camera and the location of the laser. Such a triangulation method refers to a method of converting the distance between the laser and the laser light reflection location (location of the object), and the distance between the laser and the camera into a geometrical relationship, and obtaining the required distance.
A structured-light distance sensor is a device for sensing the distance to an obstacle using a camera and a line laser, as shown in FIG. 1. For this operation, an optical filter is attached to the camera to eliminate external noise. However, there is a problem in that, since the thickness of a laser pattern varies according to distance, the range of distance detection is narrow. Further, when the reflected pattern of the structured light appears on the camera, or when noise caused by a surrounding environment (for example, natural light) occurs, the function of the structured-light distance sensor can be impaired.
As an image processing method in a system using structured light, a method of binarizing an input image and utilizing the histogram of the binarized image is well known. For example, a device using the above method (for example, a vehicle) is constructed so that a laser and a camera are mounted on a front bumper thereof, and it radiates laser light onto an object and receives an image reflected from the object, as shown in FIG. 2. Further, the number of pixels having brightness above a certain level is indicated by the histogram of FIG. 3; they are counted so that a corner of the object can be recognized. However, this technology is problematic in that, since it can be applied only to the case where a detection target is simple, and only to an object which can be easily tracked after first being detected, it is difficult to apply the technology to household robots.